How does this skill handle data contracts?

It provides Python and YAML templates for defining schemas, validation rules, and evolution policies to ensure data remains reliable and backward-compatible.

Can I monitor data freshness with this skill?

Yes, it includes built-in patterns for checking freshness, completeness, and availability SLAs against your Databricks tables.

Is this skill compatible with Unity Catalog?

Absolutely, it includes patterns for persisting product metadata and governance controls directly into Unity Catalog for enterprise-wide discovery.

What is a data product in this context?

A data product is a self-contained, discoverable data asset with clear ownership, documented contracts, and guaranteed SLAs for consumer use.

Does it support Delta Live Tables (DLT)?

Yes, the skill provides specific implementation patterns for building data products using the DLT framework with integrated quality expectations.

Data Product Engineering

Name: Data Product Engineering
Author: vivekgana

byvivekgana

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Database Management

Implements robust data mesh architectures with standardized contracts, SLAs, and governance frameworks on Databricks.

About

This skill provides specialized patterns and implementations for building self-serve data platforms based on Data Mesh principles. It streamlines the creation of high-quality data products by offering templates for schema contracts, automated SLA monitoring for freshness and completeness, and centralized metadata management for discoverability. Ideal for organizations scaling data operations, it ensures clear ownership, versioned interfaces, and production-grade reliability for analytical assets by leveraging Databricks and Unity Catalog features.

Key Features

Ready-to-use Delta Live Tables (DLT) implementation patterns
Multi-layered governance patterns including PII classification and retention
2 GitHub stars
Programmable SLA monitoring for data freshness, accuracy, and availability
Automated data contract validation and schema evolution policies
Standardized metadata registration for Unity Catalog discovery

Use Cases

Establishing clear data ownership and discoverable assets in an enterprise catalog
Automating data quality checks and SLA enforcement for downstream consumers
Scaling decentralized data architecture through Data Mesh implementation

About

Key Features

Ready-to-use Delta Live Tables (DLT) implementation patterns
Multi-layered governance patterns including PII classification and retention
2 GitHub stars
Programmable SLA monitoring for data freshness, accuracy, and availability
Automated data contract validation and schema evolution policies
Standardized metadata registration for Unity Catalog discovery

Use Cases

Establishing clear data ownership and discoverable assets in an enterprise catalog
Automating data quality checks and SLA enforcement for downstream consumers
Scaling decentralized data architecture through Data Mesh implementation